System and method for dynamic maximal iteration

Abstract

A system and method for decoding a low-density parity-check (LDPC) codeword. The method includes updating a portion of a plurality of variable nodes and performing a partial parity check based on the updated variable nodes. Based on the result of the partial parity check and an iteration condition, a determination is made as to whether to report a decoding result. The method further includes updating a plurality of check nodes and a parity check may be performed. Based on the parity check, a determination as to whether to report a decoding result is made. The iteration number may then be checked for a match with an iteration condition to determine whether to report a decoding result.

Description

CROSS REFERENCE TO RELATED APPLICATION[0001]This application is related to commonly assigned, co-pending application Ser. No. 12 / 346,043, filed on Dec. 30, 2008, entitled “DYNAMICALLY SCALED LLR FOR AN LDPC DECODER”, by Qifan Chen, et al.FIELD OF THE INVENTION[0002]The present invention is generally related to ECC communications systems.BACKGROUND[0003]Many different types of information transmission systems utilize error correction techniques to improve their efficiency. This is particularly true for those systems that need to reliably function across inherently noisy communications channels. Examples include wired systems for transmitting and receiving across noise prone physical communications lines (e.g., cable modems, DSL modems, and the like) and wireless systems for transmitting and receiving wirelessly via RF (e.g., wireless modems, wireless access points, wireless routers, and the like). Each of the above examples use an error correction system to improve efficiency.[0004]A...

AI Technical Summary

Benefits of technology

[0013]In this manner, embodiments of the present invention implement a way to decode low-density parity check (LDPC) codewords with increased performance. The increased performance may achieved via par

Problems solved by technology

This is particularly true for those systems that need to reliably function across inherently noisy communications channels.

The codes are designed so that it would take an “unreasonable” amount of noise to trick the receiver into misinterpreting the data.

Although LDPC and other error correcting codes cannot guarantee perfect reconstruction of a transmitted signal, the probability of lost information can be made very small.

However, the belief propagation algorithm is problematic in that it can be computationally demanding.

This property

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